Preprint Article Version 1 This version is not peer-reviewed

An Efficient Preprocessing Algorithm for Image-based Plant Phenotyping

Version 1 : Received: 13 April 2018 / Approved: 16 April 2018 / Online: 16 April 2018 (11:42:27 CEST)
Version 2 : Received: 20 April 2018 / Approved: 24 April 2018 / Online: 24 April 2018 (17:02:18 CEST)

How to cite: Attari, H.; Ghafari-Beranghar, A. An Efficient Preprocessing Algorithm for Image-based Plant Phenotyping. Preprints 2018, 2018040209 (doi: 10.20944/preprints201804.0209.v1). Attari, H.; Ghafari-Beranghar, A. An Efficient Preprocessing Algorithm for Image-based Plant Phenotyping. Preprints 2018, 2018040209 (doi: 10.20944/preprints201804.0209.v1).

Abstract

Plants are such important keys of biological part of our environment, supply the human life and creatures. Understanding how the plant’s functions react with our surroundings, helps us better to make plant growth and development of food products. It means the plant phenotyping gives us bio information which needs some tools to reach the plant knowledge. Imaging tools is one of the phenotyping solutions which consists of imaging hardware such as the camera and image analysis software analyses the plant images changings such as plant growth rates. In this paper, we proposed a preprocessing algorithm to eliminate the noise and separate foreground from the background which results the plant image to help the plant image segmentation. The preprocessing is one of important levels has effect on better image segmentation and finally better plant’s image labeling and analysis. Our proposed algorithm is focused on removing noise such as converting the color space, applying the filters and local adaptive binarization step such as Niblack. Finally, we evaluate our algorithm with other algorithms by testing a variety of binarization methods.

Subject Areas

plant phenotyping; noise filtering; binarization; accuracy evaluation; connected components

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